Surgical 3D reconstruction is a critical area of research in robotic surgery,
with recent works adopting variants of dynamic radiance fields to achieve
success in 3D reconstruction of deformable tissues from single-viewpoint
videos. However, these methods often suffer from time-consuming optimization or
inferior quality, limiting their adoption in downstream tasks. Inspired by 3D
Gaussian Splatting, a recent trending 3D representation, we present EndoGS,
applying Gaussian Splatting for deformable endoscopic tissue reconstruction.
Specifically, our approach incorporates deformation fields to handle dynamic
scenes, depth-guided supervision to optimize 3D targets with a single
viewpoint, and a spatial-temporal weight mask to mitigate tool occlusion. As a
result, EndoGS reconstructs and renders high-quality deformable endoscopic
tissues from a single-viewpoint video, estimated depth maps, and labeled tool
masks. Experiments on DaVinci robotic surgery videos demonstrate that EndoGS
achieves superior rendering quality. Code is available at
this https URL

应用高斯喷洒方法进行可变形内窥镜组织重构，采用变形场处理动态场景、深度引导监督优化三维目标并使用时空权重掩模减轻工具遮挡，从单视角视频、估计的深度图和标记的工具掩模中重建和呈现高质量的可变形内窥镜组织。